Title :
A Framework for Automatic Modeling from Point Cloud Data
Author_Institution :
Immersive & Creative Technol. Lab., Cyprus Univ. of Technol., Limassol, Cyprus
Abstract :
We propose a complete framework for the automatic modeling from point cloud data. Initially, the point cloud data are preprocessed into manageable datasets, which are then separated into clusters using a novel two-step, unsupervised clustering algorithm. The boundaries extracted for each cluster are then simplified and refined using a fast energy minimization process. Finally, three-dimensional models are generated based on the roof outlines. The proposed framework has been extensively tested, and the results are reported.
Keywords :
pattern clustering; solid modelling; 3D modeling; automatic modeling; fast energy minimization process; point cloud data; unsupervised clustering algorithm; Clustering algorithms; Covariance matrices; Data models; Solid modeling; Surface treatment; Three-dimensional displays; Vectors; 3D modeling; Three-dimensional reconstruction; clustering; point cloud; segmentation; shape refinement; Algorithms; Artificial Intelligence; Computer Simulation; Image Interpretation, Computer-Assisted; Models, Theoretical; Pattern Recognition, Automated;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2013.64
